Image analysis and radar detectors

ABSTRACT

An apparatus for a vehicle includes a radar detector configured to detect a police radar signal and a receiver configured to receive information about an image of an environment of the vehicle. A controller in communication with the radar detector and the receiver is configured to change at least one operating characteristic of the radar detector based on the received information.

FIELD OF THE INVENTION

The present disclosure relates generally to police radar detectors usedin motor vehicles and, more particularly, to complementing radardetector functionality with image analysis techniques.

BACKGROUND

Radar signals have been commonly used by police for some time todetermine the speed of motor vehicles. In response to radar speedmonitoring and to signal motor vehicle operators when such monitoring istaking place, police radar detectors have likewise been used for almosta coincident period of time. Currently available radar detectorsindicate the presence of radar signals, the frequency band of detectedsignals, the direction from which the radar signals originate and therelative field strength of detected signals. In addition, the radardetectors can also display information about their mode of operation andthe number of detected radar signals at any given time. The widelyvarying operating procedures for using police radar and theproliferation of other signals assigned to the same frequency bands aspolice radar has led to the need for police radar detectors which givemore information than has been provided in the past.

Additionally, vehicle based cameras have become more prevalent to assista driver in detecting potential hazards while backing-up, changing lanesor otherwise operating the vehicle. Similarly, the use of hand-helddevices with considerable processing capabilities has become almostubiquitous. These additional technologies offer new opportunities forincreasing the different types of data that can be shared with a radardetector and for enhancing operation of a radar detector based on thatdata.

SUMMARY

One aspect of the present disclosure relates to an apparatus for avehicle that tides a radar detector configured to detect a police radarsignal and a receiver configured to receive information about an imageof an environment outside or inside the vehicle. A controller incommunication with the radar detector and the receiver is configured tochange at least one operating characteristic of the radar detector basedon the received information.

Another aspect of the present disclosure relates to a method foroperating a radar detector in a vehicle that includes scanning aplurality of frequencies, by the radar detector, to detect a policeradar signal and receiving information about an image of an environmentoutside or inside the vehicle. The method also includes changing atleast one operating characteristic of the radar detector based on thereceived information.

BRIEF DESCRIPTION OF THE DRAWINGS

While the specification concludes with claims particularly pointing outand distinctly claiming the invention of the present disclosure, it isbelieved that the present disclosure will be better understood from thefollowing description in conjunction with the accompanying Figures, inwhich like reference numerals identify like elements, and wherein:

FIG. 1 provides a high-level functional block diagram of an environmentin which a vehicle-based police radar detector can operate in accordancewith the principles of the present disclosure;

FIG. 2 depicts a flowchart of an exemplary method of complementing radardetector functionality with image analysis techniques in accordance withthe principles of the present disclosure;

FIG. 3 depicts a flowchart of an exemplary framework with six conceptualsteps for complementing radar detector functionality with image analysistechniques in accordance with the principles of the present disclosure;and

FIG. 4 is a block diagram of a data processing system in accordance withthe principles of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description of the preferred embodiment,reference is made to the accompanying drawings that form a part hereof,and in which is shown by way of illustration, and not by way oflimitation, specific embodiments in which the invention may bepracticed. It is to be understood that other embodiments may be utilizedand that changes may be made without departing from the spirit and scopeof the present disclosure.

An exemplary radar detector capable of detecting radar signal strengthand the direction of the radar signal source is fully described in U.S.Pat. No. 5,083,129, which is assigned to the same assignee as thepresent application and is incorporated herein by reference in itsentirety. Also, a multi-band radar detector capable of determining arelative direction of a radar source is more fully described in U.S.Pat. No. 7,450,051, which is assigned to the same assignee as thepresent application and is incorporated by reference herein in itsentirety.

FIG. 1 provides a high-level functional block diagram of an environmentin which a vehicle-based police radar detector can operate in accordancewith the principles of the present disclosure. In particular a policeradar detector 102 similar to those described in the above-referenced,and incorporated, patents can be present that monitors one or morepolice radar bands to sense radar signals incident upon a receiver thattypically comprises one or more antennas (e.g., a generally forwardlydirected antenna and a generally rearwardly directed antenna). Although,it is to be understood that the antenna directions can be different forgiven applications and as a result of existing or future requirements.Signals received by the antennas can be passed to a switching circuitthat can connect signals from the antennas to a detector circuit. Thedetector circuit can generate radar identification signals identifyingincoming radar signals. As is known in the art, the detector circuit candifferentiate between likely police radar signals and nuisance radarsignals emanating from other radar sources.

As described in detail in the above-incorporated patents, the radardetector 102 comprises any appropriate radar detector circuit capable ofgenerating a received signal strength indicator (RSSI) output signalwhich indicates the signal strength of radar signals detected by thedetector circuit. The switching circuit and radar detector circuit cantake a wide variety of forms and can include amplifiers, mixers,diplexers, and other circuitry commonly used in the radar detector fieldas are well known to those skilled in the art. Also, a microprocessor,or similar processing device, can control the switching circuit toselectively connect signals from the antennas to the detector circuit.Operation and control of the detector circuit, for example for thedetection of radar signals in different frequency bands allocated topolice radar signals, are also performed by the microprocessor. Themicroprocessor can additionally control alarm circuits to communicateinformation regarding detected radar signals to the operator of a motorvehicle utilizing the radar detector 102 by means of one or more alarmtones and/or visual indicators which are included within the alarmcircuits. In particular, the microprocessor can generate control signalsfor the alarm circuits and any visual displays whether internal orexternal to the radar detector 102.

Along with the radar detector 102 there can be additional radar detectoraccessories 104 that communicate with the radar detector 102. Exampleaccessories 104 can include remote displays, communications modules(e.g., Bluetooth capabilities, proprietary network protocols, etc.),power supply modules, OBD-II connectors, and control modules that affectoperation of the radar detector.

Typically the radar detector 102 and radar detector accessories 104 areprovided by the same manufacturer or closely associated businesses. Inaddition to the accessories 104, other third-party devices 106 may alsobe present in the environment of FIG. 1. These devices can includelaptop computers, hand-held computer devices, smart-phones, tablets, andthe like. These devices 106 generally have their own operating systemand run applications or “apps” that allow communication with one or moreof the other components (for example, the radar detector 102) shown inFIG. 1. In particular, the devices 106 may typically include a displaythat provides a user interface whereby an operator of the vehicle canprovide data or instructions to the radar detector 102 and can viewinformation received by one or more of the devices 106.

A camera 108 is depicted in FIG. 1 and can include a wide variety ofdifferent image capturing devices. The camera 108 can also comprise aplurality of different cameras located at various positions within oroutside the vehicle. One of ordinary skill will readily recognize thatthe camera 108 may be part of one of the devices 106, for example asmartphone, or may be one of the accessories 104 available from a makerof the radar detector 102. Thus, although the camera 108 is shown as aseparate functional block in FIG. 1 it may physically be a part of oneof the other functional blocks depicted and may be referred to herein asa camera or as cameras.

The vehicle can also include its own sensors and bus 110. For example,signals from sensors or devices attached to a CAN-bus or OBD-H bus canbe available to the devices 106, the radar detector 102, the accessories104, and the other functional blocks shown in FIG. 1. Similarly, devicesimplementing these functional blocks can also transmit information overthe vehicle bus as well. Specific brands of vehicles can also provideadditional communications busses and techniques (e.g., FORD SYNC) thatcan be utilized in accordance with the principles of the presentdisclosure. For example, many vehicles include an infotainment screenthrough which vehicle, and other, information is conveyed to vehicleoccupants and this capability can also be used by devices implementingthe various functional blocks of FIG. 1.

Processing and analysis of images from one or more cameras 108 can beaccomplished via an image analyzer 114. As explained in more detailbelow, the image analyzer 114 functional block encompasses processingsoftware and hardware that can receive images (either individual imagesor a continuous stream of images), filter them, perform image analysisfunctions on the images, perform object, scene or textual recognitionwithin an image scene, and provide metadata about the contents of animage scene. The image analyzer 114 is shown as a separate functionalblock in FIG. 1 but may actually be a part of one (or more) of thecameras 108, part of one (or more) of the third-party devices 106 or one(or more) of the accessories 104. If the radar detector 102 is providedwith sufficient processing capability so that its primary functions ofdetecting radar signals and providing alerts are not adversely affected,the image analyzer 114 may be part of the radar detector 102.

As suggested above, all the capabilities and functions of the imageanalyzer 114 do not have to be accomplished by a single, separatephysical device. For example, the camera(s) 108 may provide capabilitiesto filter images such that saturation and hue are normalized in a waythat can assist in later image analysis. Smoothing and averagingtechniques can also be employed to remove noise from images captured bythe camera(s) 108 before additional analysis by the image analyzer 114is performed.

Data storage 112 can be present in the environment of FIG. 1 as aseparate storage device that is accessible by one or more of the otherdepicted functional blocks. Additionally, each of the functional blockscan have its own internal data storage as well even though it is notexplicitly illustrated in FIG. 1. The data storage 112 and/or anyinternal data storage can store images, portions of images, models ofone or more objects useful for analyzing image scenes, operatingprofiles of the various functional blocks (e.g., resolution and framerate of camera 108, scanning frequency profiles for the radar detector102, etc.), and information about what devices 106 and what accessories104 are present within the environment of FIG. 1. The data storage 112can also store information about highways, known speed traps, postedspeed limits, construction information, and other potentially transientdata for which a user can download updates.

Data storage 112 and/or internal data storage of the radar detector 102,one of the accessories 104 or one or more of the other depictedfunctional blocks can include rules or instructions that are associatedwith particular images or portions of images. For example, a rule may beto mute an alarm or warning based on a speed limit sign detected in animage. Various rules and instructions of this type are discussed indetail below but, in general, can be considered as specifying howoperation of a radar detector, its display, or warnings can be adjustedbased on analysis of an image by the image analyzer 114.

Timers and/or clocks 118 may also be beneficial to communicate with oneor more of the other functional blocks of FIG. 1. Also, one of ordinaryskill will recognize that one or more of the other functional blocks mayalready include a clock or a timer such that a separate device orsoftware is not necessary to provide such functionality. A clock can, ofcourse, provide information about a time of day but it can also be usedto determine an amount of time between two events. However, additionaltimers can also be used if the timing of multiple events is desired.

As for communication of information between the various functionalblocks of FIG. 1, a communications network 116 is illustrated asproviding this functionality. However, one of ordinary skill willrecognize that the network 116 can be implemented in a variety ofdifferent ways without departing from the scope of the presentdisclosure. For example, the accessories 104 can communicate with theradar detector 102 using a proprietary protocol and proprietaryconnectors. Also, one of the accessories 104 can be a bridge ortranslator that can communicate via non-proprietary methods (e.g., usingBLUETOOTH, via the OBD-II bus, etc.). Thus, one example of physicalconnectivity that can be provided by the communications networkabstraction represented by the communications network 116 of FIG. 1 isfor the camera 108 to be a vehicle camera connected to a CAN bus towhich one of the accessories 104 interfaces. Another one of theaccessories 104 can provide a BLUETOOTH interface to which at least oneof the third-party devices 106 can pair. An application on one of thedevices 106 can provide a user interface which displays images from thecamera 108 and controls for the radar detector 102. Thus, thecommunications network 116 in FIG. 1 is meant to encompass both directand indirect paths of communication between the various functionalblocks of FIG. 1.

FIG. 2 depicts a flowchart of an exemplary method of complementing radardetector functionality with image analysis techniques in accordance withthe principles of the disclosure of the present application. In step202, an image or a series of images are captured using an imageacquisition device such as a camera. If a series of images are capturedthen the following image analysis steps can be performed on each imagein the series. Based on the processor and computing capabilitiesallocated for image analysis, fewer than all the images of a series maybe analyzed. For example, if images are captured at a rate of 30 framesper second (fps), beneficial results of image analysis may be achievedby only analyzing 10 fps. Thus, every third frame in the series ofimages is analyzed and the others ignored. The size of an image (e.g.,the number of pixels) and/or the color depth of the image (e.g., 24-bit)may be considered when determining how many frames in a series of imagesto analyze.

In the discussion herein, the terms “frame” and “image” are usedinterchangeably to refer to one image in a series of images. Also, thephrase “image scene” and “image” can be interchanged but an “imagescene” is generally considered a higher level of abstraction because an“image” is generally comprised of pixels while an “image scene” iscomprised of objects such as “signs”, logos”, “vehicles”, etc. However,describing an “image” as having particular text (e.g., “speed limit”) ismerely a shorthand way of describing the “image scene” as having thattext.

An optional step 204 can be performed which pre-processes or filters animage (e.g., apply a Gaussian filter, correct color balance, etc.)before detailed analysis of the image scene is performed. The image canthen, in step 206, be communicated to an image analysis computer. Theimage analysis computer can be a general purpose processor with softwareapplications to perform various known image analysis algorithms.Alternatively, a dedicated digital signal processor (DSP) specialized toperform image analysis routines and techniques may be utilized as well.The image analysis computer may be an integral part of the imageacquisition device or may be a separate component connected via anetwork or other communications channel. In any event, an image isprovided to the image analysis computer which, in step 208, performsimage analysis of that image. As described more fully below with respectto FIG. 3, the image analysis step can be performed in two stages inaccordance with the principles of the disclosure of the presentapplication.

One of ordinary skill will recognize that there are many knownalgorithms, routines, and techniques for performing analysis of animage. Edge detection, for example, is typically used to separate animage scene into distinct objects that can be individually analyzed.Furthermore, once an object is isolated in one image, that object can betracked in additional images in a series to determine information aboutmovement of that object or movement of the image acquisition device.Optical character recognition (OCR) is another well-known technique forextracting text from an image. Known OCR techniques not only can extractindividual characters but can also organize them in words and phrases.Closely related to many OCR algorithms are natural-language processingalgorithms that allow OCR extracted text to be “understood” in thecontext in which they occur in an image scene.

Image analysis can also involve using models of known objects to aid inthe speed and accuracy of the image analysis. For example, interstatesigns, mile marker posts and speed limit signs all include numerals.However, the shapes (e.g., shield, very rectangular, or generallysquare) distinguish the three types of signs. Thus, a model of thesethree shapes can be stored by the image analysis computer to aid inidentifying what numerals in an image scene might refer to. Similarly,company logos, car company emblems, vehicle silhouettes, can also bestored as models that can be used when analyzing images. Thus, the datastorage 112 of FIG. 1 may store images, metadata about images, and otherdata useful in performing image analysis and pattern recognition.

Once an image is analyzed in step 208, the image analysis computer cangenerate metadata about the image based on the extracted features andtext. For example, metadata about an image can include: “the image is ofa construction zone”, “the image includes a WALMART store”, “the imageincludes a BMW next to the vehicle”, “the image includes an ‘Interstate95’ sign”, “the image matches at least a portion of a previously storedimage”, “the image indicates the current speed limit is ‘70’”, etc.

Once metadata is generated, it can be communicated to radar detectorcontrol circuitry (in step 212) and potentially be communicated toseparate display(s) or warning devices (in step 216). The radar detectorcontrol circuitry can modify or adjust, in step 214, operationalbehavior of the radar detector based on the image metadata. For example,if a nearby vehicle is identified in an image that is known to havesideways crash prevention radar at a particular frequency, then theradar detector sweep routine can be adjusted to omit that frequencywhile that vehicle is nearby. Alternatively, similar metadata could beused, in step 218, to merely change the warnings and alarms such thatthe radar detector frequency sweep remains the same but any alarm orwarning that is a result of a signal detected in a particular band issuppressed.

FIG. 3 depicts a flowchart of an exemplary framework with six conceptualsteps for complementing radar detector functionality with image analysistechniques in accordance with the principles of the present disclosure.In general, this framework can be referred to as an imaging and analysissystem. Referring to the framework of FIG. 3, each of the six steps canbe described in more detail. In particular, in the description below,reference may be made to a tablet, smartphone, or other computing devicethat can execute an app that provides a user interface.

The app may be an app that communicates with a radar detector to allow auser to control the operation of the radar detector and its warnings andalarms or the app can be a separate app that provides information aboutimage analysis and pattern recognition in accordance with the principlesof the disclosure of the present application. Features described belowas configurable or user selectable can typically be accomplished usingthe user interface of the app. However, in addition, some hardwaredevices may have their own capabilities to change configurable settingsseparate from the app.

The framework of FIG. 3 includes six major functions: capture images(step 302), perform image analysis and pattern recognition (step 304),determine a vehicle's speed (step 306), optionally determine a vehicle'sposition and direction (step 308), notify a vehicle operator about achange in the environment (step 310), and modify a radar detector'sbehavior based on the image analysis (step 312).

Capture Images (Step 302)

As mentioned above, an image or a series of images can be captured via asmartphone camera, a vehicle's imaging system, a camera included as partof a radar detector, a camera that is part of a third party device or acamera that is part of a radar detector accessory.

A camera can acquire an image or a series of images but an accessory, athird party device, or an app on a smartphone can not only acquireimages but also analyze the images to provide metadata within an image.For example, an accessory with a camera may be developed that canprovide an image and/or image metadata to a radar detector orapplication. Because the radar detector manufacturer and the accessorydeveloper are likely closely associated, the transfer of images andmetadata can be accomplished using proprietary software, hardwareinterfaces, and network protocols. For third party devices, the devicewould likely include a published application programming interface (API)or an industry standard transfer protocol to provide images and/or imagemetadata.

Additionally, images can be captured by one or more of these devices ina variety of different formats. Each image format may have its ownbenefits and drawbacks, so the image format used when capturing an imagecan vary depending on the image source (e.g., camera, smartphone,accessory), the available resources of that device and any imageanalysis processors, and the overall architecture of the imaging andanalysis system. Characteristics related to image format that can beconsidered include image quality, usage of processing resources, andavailable data transfer rate.

It is beneficial to capture the smallest image that will still allowaccurately recognizing any desired patterns and will reduce or minimizethe time and memory needed for encoding, decoding and analyzing theimage. Another consideration is the time required to transfer the imagefrom an image acquisition device, or image capture device, to the imageanalysis system. If the image capture device and analysis system aretogether, in a smart phone for example, then this consideration is notas critical as when a vehicle bus or other network connection is used totransfer captured images.

Raw image data, for example, may not require significantencoding/decoding, but may have a larger data size than variouscompressed image formats and, therefore, will take longer to transfer.MJPEG streams are relatively easy to encode/decode but typically havelarger sizes than other streaming methods and, thus, will take longer totransfer. There are also temporal encoded formats (e.g., MPEG-4, H.264)that can reduce transfer times but utilize increased resources forencoding/decoding. Any of these three types of image formats, as well asothers, are contemplated within the scope of the disclosure of thepresent application.

As for sampling rate (i.e., how often are images captured), this can bea user configurable setting and can be selected to balance betweenaccuracy, response time and resource use. The sample rate may also bedynamically changed based on the current environment. A lower samplerate will tax system resources less but have a relatively slowerresponse time that may miss some changes to a driving environment. Ahigher sample rate will have a faster response time but will utilizesystem resources more heavily. Thus, the sample rate may vary based onthe current speed of a vehicle with faster speeds corresponding tofaster sample rates.

In addition to different formats, color images as well as gray scaleimages may be utilized in accordance with the principles of thedisclosure of the present application. While color image sizes willtypically be greater than that of gray scale images, distinguishingcolor may be beneficial in more accurately analyzing image scenes. Forexample, in addition to different shapes, interstate signs are generallya different color (e.g., blue) than exit signs (e.g., green).

Perform Image Analysis (Step 304)

Image analysis can include traditional techniques for extractingobjects, text, and metadata from an image and can also includeperforming pattern recognition on the metadata to determine how anenvironment of a radar detector (or the vehicle with the radar detector)is changing.

Thus, image analysis and pattern recognition are closely related to oneanother. Image analysis can be performed on a current image or frame sothat metadata about that image can be determined. This metadata can thenbe compared to that of earlier analyzed images in order to determinesimilarities between the current image and previously captured images.For example, street signs can be identified within an image and the textof the names of the street can be extracted. Based on the differentstreet names that are visible in the image, a search can be performedfor whether those same street names are associated with any previousimages. Thus, rather than comparing image pixels to image pixels, asimilarity between two images can be accomplished by comparing metadatafrom the two images with one another (i.e., pattern recognition).

While embodiments of the disclosure of the present applicationcontemplate a wide variety of image analysis and object extractiontechniques that are known in this field, specific categories of imageanalysis contemplated include optical character recognition (OCR) thatcan identify letters, words, and textual context of certain words; edgedetection; and object detection.

For example, determining the proximity of words to each other allowsrecognizing that “Exit 31” is different than “Exit Now To Visit BASKINROBBINS 31 Flavors”. In addition to known word position relative to oneanother, a position of text within an image may also provide usefulmetadata for analysis and pattern recognition. The “position of text”within an image encompasses both a location in the image itself (e.g.,top-right quadrant, middle-left portion, etc.) but also can include itsrelative position to other image objects such as, for example, edgeswithin the image. For example, the numerals “60” may be determined to bewithin the context of four edges that generally form a rectangle. Thus,the metadata of the image might include information that identifies thatthe image includes “60” as text on a roadside sign. Depending on whetherother nearby text is “Speed Limit”, “Mile”, or “Exit”, the text can beused to determine if the “60” refers a speed limit, a mile maker, or anexit number and that information stored as metadata as well.

Object detection and analysis refers to a number of different methodsand techniques to identify various objects that might be in a capturedimage scene. Vehicle silhouettes can help identify the presence ofnearby construction vehicles, for example, or to identify a make and/ormodel of a nearby vehicle. Identifying store logos, vehicle logos andvarious trademarked symbols can be used, for example, to determine atype of vehicle nearby or if a store or other spurious radar source isnearby.

One use of edge detection may be to help reduce false triggers bymodifying which signals the radar detector generates alerts for. Forexample, WALMART and other stores are a common source of false alerts incertain frequency bands. Therefore, if image analysis and patternrecognition performed on an image reveals the presence of a WALMARTsign, then the radar detector's behavior can be adapted to mute or hidealerts with certain characteristics (e.g., duration, frequency, etc.).However, it would not be beneficial to mute or hide alerts when thevehicle is following a WALMART truck with the WALMART logo on its back.Edge analysis of the image and the structure surrounding the WALMARTtext in the image can be used to determine if the context is that of asign or a truck. Alternatively, the relative speed at which the WALMARTtext is moving between image frames can also be used to distinguishbetween a sign and a truck. If the WALMART text has an apparent movementnear the speed that the vehicle is traveling, then the text is likelypart of a sign. However, if the apparent speed the text is moving iscloser to zero, then the text is probably part of a logo on a nearbytruck traveling in the same direction as the vehicle.

Another example use of edge detection can be to help identify road signssuch as, for example, identifying the shield outline commonly used toindicate U.S. interstates. Information about an interstate on which thevehicle is traveling may be used in conjunction with other data toestablish a location and/or direction of the vehicle. For example, theinterstate number may be extracted from an image and a database lookupreveals whether it is a north/south route or an east/west route. Imagemetadata about a most-recently encountered mile-marker data can indicatea location of the vehicle and metadata from a series of images canreveal whether the mile-marker numbers are increasing or decreasing,thereby indicating a direction of the vehicle.

In the description herein, “scene recognition” can be considered tofocus mainly on determining a similarity between at least portions oftwo images. As is known in the art, the image pixel data from one imagecan be compared to the image pixel data from a different image and ascore, or confidence value, can be calculated which indicates similaritybetween the two sets of image pixel data. Typically, an image can besegmented into different objects or regions and two images that have asimilar set of objects are considered to be similar. The phrase “patternrecognition”, as used herein, focuses more on the metadata that isassociated with, or extracted from, an image. Thus, “scene recognition”and “pattern recognition” can be used together or separately to analyzean image and determine if it is similar to, or “matches”, a previouslyencountered image.

Lighting difference between day and night and seasonal changes mayaffect the scene recognition more than pattern recognition involvingmainly metadata. In pattern recognition steps, reliance on street signswill likely not be as adversely affected by changing conditions as scenerecognition steps that rely on more than just the metadata. For example,extracting the metadata that indicates the image includes a sign withthe text “Speed Limit 55” will allow recognition of the current speedlimit (and possibly change a radar threshold value). Street signs aredesigned to have high contrast in all conditions, so the OCR algorithmsutilized as described herein are likely to work well under most drivingconditions. However, recognizing a scene can require more than just OCR.For example, a snow covered intersection on a cloudy January eveninglooks radically different than the same intersection on a bright day inJuly at noon.

Thus, when comparing scenes, a confidence spectrum between 0% and 100%can be used to describe whether or not two scenes match. A 0% confidencelevel means there is no match. A 100% confidence level means there is anexact match. A predetermined threshold confidence level can then bedefined to determine when to take action for a recognized scene (e.g.,mute alerts). This predetermined action threshold can be configured bythe user or hard coded into the system.

One example method to perform scene recognition is to do so in twostages. The first stage of scene recognition would be to compare themetadata associated with the current image to the metadata for a storedimage. Once a current image is analyzed and its metadata identified, thecomparison of the generally textual/numerical metadata can occurrelatively quickly. This stage can be used to establish a first baselineconfidence level before comparing the actual image data. As mentionedabove, because the metadata is mainly OCR information, this stage willbe less reliant on lighting conditions than comparison of actual images.For example, one or more recognized street names can heavily influencethe confidence level in a match between two images even under differentlighting conditions.

Using the pattern recognition steps, as described herein, various typesof street signs and their contents can be identified when such signs arein the image. Pattern recognition can involve analysis of all text inthe image in conjunction with the location of the text, wherein thelocation of the text refers to both the location of the text within theimage and the location of different portions of text relative to oneanother. For example, a captured image may be of an intersection with aWALMART and a BURGER KING on the left side of the road and a LOWES and aSAM'S CLUB on the right side of the road. As discussed above, theextracted metadata can include the location of the text, so we can usethe location of the sign names to increase a confidence level in amatch.

A second stage of a scene recognition algorithm in accordance with theprinciples of the disclosure of the present application can use variousimage matching algorithms as are known in the art. One of ordinary skillwill recognize that various image matching algorithms are known to havedifferent strengths and weaknesses. Some algorithms are more tolerant ofchanging light conditions, some are more tolerant of image noise, andsome are known for processing speed. Thus, in accordance with theprinciples of the disclosure of the present application, different imagematching algorithms can be available and selected according to a currentdriving environment. While one “standard” algorithm can be relied uponfor comparison of most images, other image matching algorithms can beselected if certain environmental conditions are present (e.g., thecurrent speed of the car is greater than a predetermined speed, or it isnighttime). In addition to the daylight conditions and other environmentconditions, the matching algorithm's speed can be a consideration. Abalance between a fast response time and accuracy under differentconditions is beneficial in most instances. The type of processingcapabilities of the device that analyzes captured images can also be afactor in selecting one image matching algorithm for certainimplementations and another matching algorithm for differentimplementations.

Based on how similar the image matching algorithm determines a currentimage is to a stored image, the first baseline confidence level can beadjusted either up or down. This adjusted confidence level can then becompared to the predetermined threshold to determine if a matchoccurred.

Because the image matching analysis can take a longer time relative todetermining whether or not there is matching metadata, this second stageof scene recognition can be skipped altogether if the first baselineconfidence level is below a predetermined threshold. Similarly, thesecond stage can be skipped if the first baseline confidence level is sogreat as to be above a predetermined threshold. If substantially all ofthe text from the current image matches the metadata of a stored imageand the location of the text also matches, the event, or action,associated with the stored image can be triggered without performing theactual image comparison.

The above description envisions a radar detector that can be coupledwith an image acquisition device and an image analysis processor. Inthis way, the radar detector may be provided with image metadata thatallows its own control circuitry to determine operational parameters.For example, alerts may be muted based on recognizing that the metadatarelates to known situations or circumstances that are typically falsepositives. Alternatively, the image metadata can include identificationof a current speed limit which then becomes a new threshold for when tomute or not mute alerts about detected radar signals.

Example Pattern Recognition Scenarios

As an initial step, an image of a street sign is acquired and analyzedso as to determine one or more portions of text from that street sign.Based on the extracted metadata, the following types of patternrecognition can be accomplished:

Example #1

A 35 MPH speed limit sign is recognized. The smart phone app canautomatically change the radar detector threshold to 35 MPH (e.g., via aproduct similar to Savvy® for V1® detectors). Changing the threshold inthis manner causes all alerts on the radar detector to be muted if thevehicle is traveling 35 MPH or less.

Example #2

A “Radar Detectors Illegal” sign is recognized. Using the smart phoneapp, the user is notified that radar detectors should not be used inthat area.

Example #3

A photo radar warning sign is recognized. The user is notified aboutphoto radar using the smart phone app and a radar scanning profile(e.g., temporarily disabling filtering) of the radar detector can bemodified to improve detection range and response time.

Example #4

One of the “Your speed is . . . ” trailers is recognized which resultsin temporarily muting the radar detector for all K band alerts until thetrailer is no longer in an image.

Example #5

As described above, using mile markers and road designation (i.e. I-75South) signs, the vehicle's current location can be determined. Thisinformation can be used in conjunction with a database to determine thespeed limit, change the detector's behavior or notify the driver ofspeed traps.

In the above examples, the metadata from an image can be used to controloperation of a radar detector or accessory without necessarilyperforming scene recognition to identify a previously stored matchingimage. Accordingly, certain metadata (e.g., speed limit information)that is pertinent regardless of the specific location of a vehicle canbe stored such that it is not associated with any previously storedimage. Thus, when an image is captured a determination can be madewhether or not the current metadata from that image matches any of thestored metadata that is not necessarily associated with a specificstored image. Thus, any events, or actions, associated with thatmetadata can be triggered regardless of whether or not a current imagematches a stored image.

One of ordinary skill will recognize that the above examples are merelyprovided to illustrate how broad a scope of information, or metadata,can be extracted from images and used in conjunction with a radardetector. Other categories of signs, for example, that may be ofinterest to radar detector usage can include school zone, constructionzone, and traffic information.

In addition to pattern recognition of signs, pattern recognition todetermine the brand and/or model name on the back of a nearby vehicle,or pattern recognition to recognize the vehicle profile from the frontor rear, may be beneficial. Based on the vehicle detected, the way theradar detector behaves can be controlled because some vehicles are wellknown to cause false alarms.

Construction zones provide some interesting challenges for a radardetector system. They are a common source of false alarms, but they arealso a common place for speed enforcement. Recognizing a constructionzone can allow fine tuning behavior of the detector for thisenvironment. For example, one possible configuration would be to muteone radar band and increase the sensitivity on another one. One ofordinary skill will recognize a number of other techniques or strategiescould be used to reduce the number of false positive alerts in theconstruction while not overlooking actual threats.

Pattern recognition of scenes is also contemplated within the scope ofthe disclosure of the present application. The app on the smartphone, orother device, can allow a user to mark a current image as beingassociated with a known false positive (i.e., a radar signal is detectedbut it is not associated with a police radar source). While imagemetadata (e.g., street signs, objects in the image scene) may beassociated with an image that is marked, other data such as a nearbyWi-Fi signature can be detected and stored. The Wi-Fi signature canidentify different, nearby wireless networks that are available andtheir various affiliated identifiers (e.g., BSSID, ESSID, SSID, etc.).

When a current image scene is captured it can be compared to all themarked scenes that are associated with a false positive. Based onsimilarity between the current scene image and the stored scenes (e.g.,a comparison of their metadata or their image pixels), a determinationcan be made as to whether the current scene matches one of the markedscenes. The respective Wi-Fi signature associated with each of themarked scenes can be used as secondary verification of the scenerecognition. Thus, when the current image matches one of the marked,stored images, the radar detector's behavior can be modified so as toeliminate the false alarm, for example, or to perform whatever event hasbeen associated with that marked image.

For opposite purposes, the user can mark a current scene as a knownradar trap location. When the current scene matches one of those storedscenes marked as a known trap, the user can be notified and the radardetector's behavior can be changed to improve detection.

In general, scene analysis, or scene recognition, takes as input aseries of images, each of which is analyzed. The analysis extractsfeatures and metadata associated with an image and determines if theimage matches an image that has previously been marked and stored. Asmentioned above, metadata about the image rather than the entire pixelinformation of the image may be stored and non-image data may beacquired (e.g., a nearby Wi-Fi-signature) to be stored in associationwith the image. When marking an image using the app, the user ispresented with a variety of choices of how to describe the image scene(e.g., a false positive, a known speed trap, etc.). Based on thatdescription associated with a marked image, the radar detector'sbehavior can automatically be modified when a matching image is onceagain encountered. The modification of the radar detector's behavior caninclude muting/unmuting alarms, increasing sensitivity, filteringcertain bands, and the like.

Embodiments of the disclosure of the present application alsocontemplate a number of ways to store current operating attributes of aradar detector and then restore the radar detector's behavior to the wayit behaved before a particular scene was recognized. For example, theradar detector's behavior can be restored after a predetermined amountof time has elapsed since a matching scene was first detected, or theelapsed time can be measured from the last time a matching scene wasdetected. Alternatively, such as for example, when the matching sceneinvolves a store sign (e.g., WALMART, WALGREENS, etc.) the modifieddetector behavior can last only during the period in which the sign isrecognized being present in a current image.

One of ordinary skill will recognize that there are more complex ways todetermine how a radar detector's behavior can be modified based onscene, or pattern, recognition. For example, when muting alerts based ona speed limit sign, the muting can be stopped if: a) a speed limit signis not recognized for a specified time; b) a different speed limit signis recognized; or c) another pattern is recognized (e.g., false positivelocation, speed trap location, etc.) that overrides the speed limitfunctionality.

One of ordinary skill will recognize that there are a number of ways toreduce computational complexity of the image analysis contemplatedwithin the scope of the present disclosure. For example, the image scenemay include portions of the vehicle's dashboard and gauges as well asportions viewed through the front windshield of the vehicle. The whitelines, yellow lines, and dashed lines on the roadway can be used to helporient an image so that relative terms such as “top”, “lower”, “right”,“left”, etc. can be used in conjunction with an image. Fixed portions ofthe vehicle (e.g., the hood) can be used to help orient an image aswell. Other vehicles and signs are not going to be found in the part ofthe image that corresponds to the vehicle's dashboard. Highway and exitsigns are likely to be encountered in an upper region of an image (e.g.,if the sign is mounted on an overpass) or in a right-most region of theimage. Thus, analyzing and performing pattern recognition can be limitedto those regions or portions of an image where a specific object maymost-likely be encountered.

The portions of an image that are analyzed for street signs may beconfigured as part of the app during its design process. Additionally,the definitions of which portions of an image are searched for whichtypes of objects can be configured by a user. Because a user has freedomto orient a camera and, thus, has the freedom to determine the portionof the nearby environment within the field-of-view of the camera, theapp can be configured to allow the user to select which portions of animage are likely or not likely to include a particular type of object.

As one example, the app can visually present a rectangle that representsthe entire field-of-view of the camera. The user can also be presentedwith a list of objects that are useful in pattern recognition (e.g.,street sign, speed limit sign, mile marker sign, exit sign, interstatesign, vehicle profile, store logo, etc.) Once the user selects anobject, they can associate with that object a region of the image wherethat object is likely to be encountered. For example, using the userinterface of the app, the user can draw a rectangle or polygon on thefield-of-view rectangle. The image analysis processor may then limitimage analysis for that particular object to that user-specified region.One of ordinary skill will recognize that functionality can be includedwithin the app to associate more than one object with a single region orto associate an object with two different, non-contiguous regions of theimage.

The app can also include a preview screen to aid in camera mounting andaiming. The preview screen can be used with any of the types of imagestream sources discussed above. In particular, the preview screenfunctionality may not necessarily be part of the app described hereinbut can be provided by a separate, standalone app. It is beneficial thatduring use of the preview screen, text, shapes and patterns can berecognized with respect to the image(s) used when aiming and mountingthe camera.

Additionally, to reduce the amount of aiming configuration a user canperform, specific fixed mounts can be designed for particular vehiclesand particular image capture devices. Specifying the vehicle and theimage capture device will result in a priori knowledge of what the fieldof view of the image capture device will be. Also, design and use of animage capturing accessory of the radar detector manufacturer can reducethe number of possible permutations about how third-party devices can bemounted or aimed. Other options include using a device that captures apanoramic image. The wide view of these devices allows for significantvariation in the aiming of the camera. Alternatively, an accessory toaccommodate multiple cameras can be used. For example, such an accessorythat would have three cameras can be designed to be mounted on thecenter of the dashboard. There would be a left, right and center facingcamera, which would effectively increase the field of view. Theaccessory could include mounting instructions to help increase thelikelihood of capturing a beneficial field of view.

Determine Vehicle Speed (Step 306)

In some of the example scenarios described above, the speed of thevehicle is useful, in conjunction with other pattern recognitiondeterminations, in determining how to modify behavior of a radardetector. The speed of the vehicle can be determined using patternrecognition such as, for example: a) identifying mile marker signs andelapsed time between them; b) analyzing an image of the vehicle'sspeedometer; c) analyzing an image of the vehicle's odometer and anamount of elapsed time; or d) analyzing at least two images to determineapparent motion of fixed elements (e.g., a speed limit sign).

In other instances the vehicle speed information can be read from anOBD-II port, the vehicle's infotainment system (e.g., FORD SYNC), orsome other accessory of the vehicle.

Regardless of the method of determining the vehicle's speed, thisinformation can be made available to the app, to the radar detector, theimage analysis processor, and third party devices or accessories and,may in some instances, be used alone or in conjunction with other imageanalysis or pattern recognition results to determine how to modifybehavior of the radar detector or one or more accessories. For example,if the speed of the vehicle is below a preset value, then alarms,warnings and/or notifications can be muted. Also, depending on vehiclespeed, a filtering profile or a detector's sensitivity can be adjusted.

Optionally Determine Vehicle Position and Direction (Step 308)

In addition to vehicle speed, and as mentioned above, image analysis andpattern recognition can be used to determine location information byrecognizing street signs. For example, road designation signs (e.g.,I-75 South), and mile marker signs or exit number signs can be used todetermine a location and the sequence of mile marker signs can be usedto determine direction. Because exit numbers also tend to increase whiletravelling north and east, exit number signs can also be useful indetermining a direction of travel.

Once a location is determined, the radar detector's behavior can bealtered by changing one or more stored profiles. As is known in the art,different operation profiles can be stored by a radar detector (or by anapp associated with a radar detector). The profiles are stored in a waythat allows a user to retrieve a desired profile depending on thecurrent location or circumstances of the vehicle. Thus, the respectivestored profiles can be associated with a different location.

For example, when a driver is driving south on I-75 in Ohio the imageanalysis and pattern recognition system described herein can recognizewhen the vehicle is five miles from downtown Dayton. When this patternis recognized, the smart phone app changes the radar detector'soperation profile to a “Dayton” profile. If the vehicle continues southand the system subsequently recognizes a sign for I-275 West at milemarker 16, the smart phone app can change the radar detector to a“Cincinnati” profile.

Notify an Operator about an Environment Chance (Step 310)

Changing the behavior of a radar detector based on pattern recognitionas part of image analysis has been described herein. In addition tosimply changing the radar detector's behavior, an operator of thevehicle, or user of the app, can also be notified about any changes inthe environment based on the image analysis and pattern recognition. Thenotification can include audio and/or visual information and can bebeneficial in providing the operator with safety or speed enforcementinformation, hi particular, the notification can be delivered using oneor more of the devices available within the vehicle environment (asshown in FIG. 1). Such devices include, for example, an app on a smartphone (or other device), the vehicle's infotainment system, the radardetector itself, and other accessories of the radar detector.

As described herein, there are several opportunities for providing bothvisual and aural notifications to a user. In general, the notificationscan be designed to require no interaction from the user. For example, anotification will not be displayed so as to obscure the app screen in away that prompts a user to want to move the notification. Rather thanbeing dismissible (i.e., requiring a user interaction), a notificationcan timeout after a specified time. The timeout interval can bepredetermined in the system or be configurable by a user. Also, anotification can persist as long as the condition generating thatnotification exists. For example, a construction zone notification canstay on the screen until an “end construction zone” sign is captured andidentified in an image.

As described herein there are a plurality of different notificationevents that can occur (e.g., “switching to Dayton profile”, “entering aconstruction zone”, “radar detectors are illegal”, etc.) and differenttypes of notification methods (e.g., the radar detector, the app, aremote display, an accessory, etc.). Thus, the app described herein canoffer a user a variety of different configurable items. For example, aconfiguration interface presented to a user could list all the differenttypes of notifications that can occur. The user can select anotification type and then be presented with all the availablenotification methods. Accordingly, a user can select a respective set ofnotification methods that are associated with each notification event.When a notification event occurs, the user can be notified using all theconfigured notification methods associated with that particular event.

Modify Radar Detector Operation Based on Image Analysis (Step 312)

As provided in many of the above-discussed examples, when a particularpattern is recognized during image analysis, the behavior of the radardetector can be changed in a variety of different ways. A wide varietyof detector operations can be modified such as: a) the volume of thealert can be raised or lowered; b) a different visual indicator can beused such as a special, or modified, symbol on the app user interface toindicate an alert muted due to pattern recognition; c) hide the alertcompletely on the radar detector display or an accessory display; or d)in general, the information and format of the display and sound of theapp can be modified based on pattern recognition and image analysisdeterminations.

Other types of behavior modifications can include altering the logic ofthe filtering algorithms based on the current environment. For example,the verification requirements for determining a true threat can bereduced when a photo radar warning sign has recently been identified inan image.

Also, the swept RF spectrum can be changed. For example, certain partsof a radar band can be ignored if a vehicle that is known to cause falsealarms is identified in an image.

An accessory which works in conjunction with the radar detector can haveits behavior modified as well. For example, an accessory that sets aspeed threshold below which all radar alerts are muted can have itsbehavior modified as discussed above such that the speed threshold ischanged based on pattern recognition related to identifying a currentspeed limit from a speed limit sign in the current image.

Aspects of the disclosure of the present application may be implementedentirely as hardware, entirely as software (including firmware, residentsoftware, micro-code, etc.) or by combining software and hardwareimplementation that may all generally be referred to herein as a“circuit,” “module,” “component,” or “system.” Furthermore, aspects ofthe disclosure of the present application may take the form of acomputer program product embodied in one or more computer readable mediahaving computer readable program code embodied thereon.

Any combination of one or more computer readable media may be utilized.The computer readable media may be a computer readable signal medium ora computer readable storage medium. A computer readable storage mediummay be, for example, but not limited to, an electronic, magnetic,optical, electromagnetic, or semiconductor system, apparatus, or device,or any suitable combination of the foregoing. More specific examples (anon-exhaustive list) of the computer readable storage medium wouldinclude the following: a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an appropriateoptical fiber with a repeater, a portable compact disc read-only memory(CDROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer readable signal medium may be transmitted usingany appropriate medium, including but not limited to wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thedisclosure of the present application may be written in any combinationof one or more programming languages, including an object orientedprogramming language such as Java, Scala, Smalltalk, Eiffel, JADE,Emerald, C++, CII, VB.NET, Python or the like, conventional proceduralprogramming languages, such as the “C” programming language, VisualBasic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programminglanguages such as Python. Ruby and Groovy, or other programminglanguages. The program code may execute entirely or partly on a user'scomputer or device. In the latter scenario, a separate computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider) or in a cloud computingenvironment or offered as a service such as a Software as a Service(SaaS).

Aspects of the disclosure of the present application are describedherein with reference to flowchart illustrations and/or block diagramsof methods, apparatuses (systems) and computer program productsaccording to embodiments of the disclosure. It will be understood thateach block of the flowchart illustrations and/or block diagrams, andcombinations of blocks in the flowchart illustrations and/or blockdiagrams, can be implemented by computer program instructions. Thesecomputer program instructions may be provided to a processor of ageneral purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which execute via the processor of the computer orother programmable instruction execution apparatus, create a mechanismfor implementing the functions/acts specified in the flowchart and/orblock diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that when executed can direct a computer, otherprogrammable data processing apparatus, or other devices to function ina particular manner, such that the instructions when stored in thecomputer readable medium produce an article of manufacture includinginstructions which when executed, cause a computer to implement thefunction/act specified in the flowchart and/or block diagram block orblocks. The computer program instructions may also be loaded onto acomputer, other programmable instruction execution apparatus, or otherdevices to cause a series of operational steps to be performed on thecomputer, other programmable apparatuses or other devices to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

FIG. 4 depicts a block diagram of an exemplary data processing system400 such as may be utilized to implement a hardware platform that canimplement all, or portions of; an image analysis and recognition system,a device for executing an image analysis and pattern recognition app, ora radar detector accessory as set out in greater detail in FIG. 1-FIG.3. The system 400 may comprise a symmetric multiprocessor (SMP) systemor other configuration including a plurality of processors 402 connectedto a system bus 404. Alternatively, a single processor 402 may beemployed. Also connected to the system bus 404 is a memorycontroller/cache 406, which provides an interface to local memory 408.An I/O bridge 410 is connected to the system bus 404 and provides aninterface to an I/O bus 412. The I/O bus 412 may be utilized to supportone or more buses and corresponding devices 414, such as bus bridges,input output devices (I/O devices), storage, network adapters; etc.Network adapters may also be coupled to the system to enable the dataprocessing system 400 to become coupled to other data processing systemsor remote printers or storage devices through intervening private orpublic networks.

Also connected to the I/O bus 412 may be devices such as a graphicsadapter 416, storage 418 and a computer usable storage medium 420 havingcomputer usable program code embodied thereon. The computer usableprogram code may be executed to perform any aspect of the disclosure ofthe present application, for example, to implement any aspect of any ofthe methods, computer program products and/or system componentsillustrated in FIG. 1-FIG. 3.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousaspects of the disclosure of the present application. In this regard,each block in the flowchart or block diagrams may represent a module,segment, or portion of code, which comprises one or more executableinstructions for implementing the specified logical function(s). Itshould also be noted that, in some alternative implementations, thefunctions noted in the block may occur out of the order noted in theFigures. For example, two blocks shown in succession may, in fact, beexecuted substantially concurrently, or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved. It will also be noted that each block of the block diagramsand/or flowchart illustrations, and combinations of blocks in the blockdiagrams and/or flowchart illustrations, can be implemented by specialpurpose hardware-based systems that perform the specified functions oracts, or combinations of special purpose hardware and computerinstructions.

While particular embodiments have been illustrated and described, itwould be obvious to those skilled in the art that various other changesand modifications can be made without departing from the spirit andscope of the invention. It is therefore intended to cover in theappended claims all such changes and modifications that are within thescope of this invention.

What is claimed is:
 1. An apparatus for a vehicle comprising: a radardetector configured to detect a police radar signal; a receiverconfigured to receive information about an image of an environment ofthe vehicle; and a controller in communication with the radar detectorand the receiver and configured to change at least one operatingcharacteristic of the radar detector based on the received information.2. The apparatus of claim 1, comprising: an image analyzer configured toanalyze the image and determine the information about the image; and theimage analyzer further configured to communicate the information to thereceiver.
 3. The apparatus of claim 2, comprising: a camera configuredto capture the image and communicate the image to the image analyzer. 4.The apparatus of claim 1, comprising: a data storage device, incommunication with the controller, configured to store respectivemeta-information about a plurality of ambient environments potentiallyoccupied by the vehicle.
 5. The apparatus of claim 4, comprising: thecontroller further configured to identify one of the respectivemeta-information that matches the information about the image; and thecontroller further configured to change the at least one operatingcharacteristic based on the matching one of the respectivemeta-information.
 6. The apparatus of claim 1, comprising: the receiverfurther configured to receive at least a portion of the image of theenvironment; and a data storage device, in communication with thecontroller, configured to store a plurality of images.
 7. The apparatusof claim 6, comprising: the controller further configured to identifyone of the plurality of images that matches the at least a portion ofthe image of the environment; and the controller further configured tochange the at least one operating characteristic based on the matchingone of the plurality of images.
 8. The apparatus of claim 1, comprising:the receiver further configured to receive at least a portion of theimage of the environment; and a data storage device, in communicationwith the controller, configured to store respective meta-informationabout a plurality of ambient environments potentially occupied by thevehicle and configured to store a plurality of images.
 9. The apparatusof claim 8, comprising: the controller further configured to: identifyone of the respective meta-information that matches the informationabout the image; determine a subset of the plurality of images based onthe matching one of the respective meta-information; identify one of thesubset of the plurality of images that matches the at least a portion ofthe image of the environment; and change the at least one operatingcharacteristic based on the matching one of the subset of the pluralityof images.
 10. The apparatus of claim 1, comprising: a controller incommunication with the radar detector and the receiver and configured toreceive a speed of the vehicle; and the controller further configured tochange at least one operating characteristic of the radar detector basedon the received information and the speed of the vehicle.
 11. Theapparatus of claim 1, wherein the received information about the imagerelates to at least one of: an exit sign, a road identifier, amile-marker identifier, and a speed limit.
 12. The apparatus of claim 1,wherein the received information about the image relates to at least oneof: a neighboring vehicle, the environment being a construction zone, awireless network signature, and a neighboring retail establishment. 13.The apparatus of claim 1, wherein the at least one operatingcharacteristic of the radar detector relates to: muting a warning,selecting a spectrum profile, adjusting a detector sensitivity,disabling an alarm, disabling a frequency band of the detector, andsetting a radar detector speed threshold.
 14. The apparatus of claim 1,wherein the environment of the vehicle comprises an environment outsideof the vehicle.
 15. The apparatus of claim 1, wherein the environment ofthe vehicle comprises an environment inside the vehicle.
 16. Theapparatus of claim 15, wherein the image is an image of at least aportion of a dashboard of the vehicle.
 17. A method for operating aradar detector in a vehicle comprising: scanning a plurality offrequencies, by the radar detector, to detect a police radar signal;receiving information about an image of an environment of the vehicle;and changing at least one operating characteristic of the radar detectorbased on the received information.
 18. The method of claim 17,comprising: analyzing, by an image analyzer, the image to determine theinformation about the image; and communicating the information to thereceiver.
 19. The method of claim 18, comprising: capturing the imageand communicating the image to the image analyzer.
 20. The method ofclaim 17, comprising: storing, in a data storage device, respectivemeta-information about a plurality of ambient environments potentiallyoccupied by the vehicle.
 21. The method of claim 20, comprising:identifying one of the respective meta-information that matches theinformation about the image; and changing the at least one operatingcharacteristic based on the matching one of the respectivemeta-information.
 22. The method of claim 17, comprising: receiving atleast a portion of the image of the environment; and storing, in a datastorage device, a plurality of images.
 23. The method of claim 22,comprising: identifying one of the plurality of images that matches theat least a portion of the image of the environment; and changing the atleast one operating characteristic based on the matching one of theplurality of images.
 24. The method of claim 17, comprising: receivingat least a portion of the image of the environment; and storing in adata storage device: respective meta-information about a plurality ofambient environments potentially occupied by the vehicle, and aplurality of images.
 25. The method of claim 24, comprising: identifyingone of the respective meta-information that matches the informationabout the image; determining a subset of the plurality of images basedon the matching one of the respective meta-information; identifying oneof the subset of the plurality of images that matches the at least aportion of the image of the environment; and changing the at least oneoperating characteristic based on the matching one of the subset of theplurality of images.
 26. The method of claim 17, comprising: receiving aspeed of the vehicle; and changing the at least one operatingcharacteristic of the radar detector based on the received informationand the speed of the vehicle.
 27. The method of claim 17, comprising:based on the received information about the image, determining a speedof the vehicle.
 28. The method of claim 27, wherein the receivedinformation comprises information about a speedometer of the vehicle.29. The method of claim 17, wherein the received information about theimage relates to at least one of: an exit identifier, a road identifier,a mile-marker identifier, and a speed limit.
 30. The apparatus of claim17, wherein the received information about the image relates to at leastone of: a wireless network signature, a neighboring vehicle, theenvironment being a construction zone, and a neighboring retailestablishment.
 31. The method of claim 17, wherein the at least oneoperating characteristic of the radar detector relates to: muting awarning, selecting a spectrum profile, adjusting a detector sensitivity,disabling an alarm, disabling a frequency band of the detector, andsetting a radar detector speed threshold.
 32. The method of claim 17,wherein the environment of the vehicle comprises an environment outsideof the vehicle.
 33. The method of claim 17, wherein the environment ofthe vehicle comprises an environment inside the vehicle.